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Design, simulate, and test radar systems

Radar engineering teams use MATLAB® and Simulink® to design, analyze, simulate, and test multifunction radar systems. Combine Radar Toolbox with other MathWorks® products to reduce development time, eliminate design problems early, and streamline analysis and testing for airborne, ground-based, maritime, and automotive radar systems.

Antenna and RF systems, signal processing, and data processing in a development environment. Create model scenarios and scenes including resource management and controls.

With MathWorks products for radar systems, you can develop models that support the entire life cycle of radar systems:

  • Perform link budget analysis, model architectures, and evaluate system design tradeoffs.

  • Create georeferenced scenarios and simulate radar signals, detections, and tracks.

  • Design signal and data processing chains for different waveforms and phased array front ends.

  • Automatically generate HDL or C code for prototyping and deployment.


Radar Systems Engineering

Radar Scenario Simulations

  • Radar Performance Analysis over Terrain (Mapping Toolbox)
    The performance of a radar system can depend on its operating environment. This example shows how radar detection performance improves as target elevation increases above the terrain.
  • Simulate and Track Targets with Terrain Occlusions (Sensor Fusion and Tracking Toolbox)
    This example shows you how to model a surveillance scenario in a mountainous region where terrain can occlude both ground and aerial vehicles from the surveillance radar.
  • Simulated Land Scenes for Synthetic Aperture Radar Image Formation (Radar Toolbox)
    Simulate of an L-band remote-sensing SAR system by generating IQ signals from a scenario containing three targets and a wooded-hills land surface and then processing the returns using a range migration focusing algorithm.

Multifunction Radar

  • Adaptive Tracking of Maneuvering Targets with Managed Radar (Radar Toolbox)
    This example employs radar resource management to efficiently track multiple maneuvering targets. An interacting multiple model (IMM) filter estimates when the target is maneuvering to optimize radar revisit times.
  • Multibeam Radar for Adaptive Search and Track (Radar Toolbox)
    Use radarDataGenerator as part of a closed-loop simulation of a multifunction phased array radar (MPAR) tracking multiple maneuvering targets. At each update interval the radar requests resources from the MPAR to search for targets and revisit existing tracks.
  • Track Space Debris Using a Keplerian Motion Model (Sensor Fusion and Tracking Toolbox)
    This example shows how to model earth-centric trajectories using custom motion models within trackingScenario, how to configure a fusionRadarSensor in monostatic mode to generate synthetic detections of space debris, and how to setup a multi-object tracker to track the simulated targets.

Radar Antennas, Beamforming, and Waveforms

  • Modeling Mutual Coupling in Large Arrays Using Embedded Element Pattern (Phased Array System Toolbox)
    Model mutual coupling effects between array elements by using an embedded pattern technique. The example models an array two ways: (1) using the pattern of the isolated element or (2) using the embedded element pattern, and then compares both with the full-wave Method of Moments (MoM)-based solution of the array.
  • Conventional and Adaptive Beamformers (Phased Array System Toolbox)
    Apply three beamforming algorithms to narrowband array data: the phase shift beamformer, the minimum variance distortionless response (MVDR) beamformer, and the linearly constrained minimum variance (LCMV) beamformer.
  • Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
    Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Code Generation and Deployment